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Industrial AI These Five Cost Traps Hinder Automation

Source: Cloudian | Translated by AI 2 min Reading Time

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The pressure to profitably integrate AI into production is at an all-time high. However, while technological progress is rapid, many companies underestimate the long-term infrastructure costs. Cloudian has identified the five biggest budget drainers.

The biggest AI cost traps – from data preparation to unpredictable cloud fees.(Source: / Pixabay)
The biggest AI cost traps – from data preparation to unpredictable cloud fees.
(Source: / Pixabay)

The AI train is moving at full speed towards the future. As a result, even the last hesitant companies now want to jump on board. According to the company Cloudian, the fastest way to do so is undoubtedly through the cloud. However, not only does its usage have the potential to push budgets to their limits. The additional cost traps in the implementation and operation of AI have been examined by Cloudian in its Enterprise AI Infrastructure Survey.

Cost trap #1: Complex Integration of OT Data

The biggest hurdle (45 percent) is data preparation. In automation, data often resides in isolated silos or proprietary formats. Harmonizing sensor data, PLC protocols, and ERP data for AI training is time- and cost-intensive. Data quality issues are often only identified once the project is already underway.

Cost trap #2: The Battle for AI Specialists with Industry Knowledge

AI specialists are scarce and expensive (34 percent). Particularly challenging for the industry: experts are needed who not only master machine learning but also understand the physical processes in manufacturing. Recruitment and external consulting often exceed initial budgets here.

Cost trap #3: Licensing Jungle for Industrial Software

In addition to the AI platform itself, costs often accrue for specialized middleware, monitoring tools, and security licenses (31 percent). When pilot projects scale from a single machine to the entire fleet, usage-based fees often rise sharply.

Cost trap #4: Unclear Total Cost of Ownership (TCO)

25 percent of respondents struggle with unclear total operating costs. Specifically, the maintenance of AI models (retraining) when production conditions change is often overlooked during the planning phase.

Cost trap #5: Explosive Costs due to Cloud Transfer

Consumption-based cloud pricing (23 percent) is risky in automation. When high-frequency machine data is continuously streamed to the cloud for real-time analysis, data transfer and storage fees (egress charges) quickly lead to financial surprises.

If you want to make AI the DNA of your production, you need to plan foresightfully. In particular, in the industry, the cloud is great for pilot projects, but for full-scale rollout on the shop floor, on-premises or private cloud structures are often the more economical choice due to cost control and latency.

Sascha Uhl, Cloudian

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